U.S. patent number 9,768,812 [Application Number 15/179,427] was granted by the patent office on 2017-09-19 for facilitation of passive intermodulation cancellation.
This patent grant is currently assigned to AT&T INTELLECTUAL PROPERTY I, L.P.. The grantee listed for this patent is AT&T Intellectual Property I, L.P.. Invention is credited to Vibhav Kapnadak, Paul Maxwell, Ernest Tsui.
United States Patent |
9,768,812 |
Tsui , et al. |
September 19, 2017 |
Facilitation of passive intermodulation cancellation
Abstract
A passive intermodulation detection system is provided to
remotely identify passive intermodulation at a base station site
and diagnose the type of intermodulation and location of the
non-linearity that is the source of the passive intermodulation.
The passive intermodulation detection system can generate a test
signal in a first band that is transmitted by an antenna. Another
antenna can receive a signal in another band, and the passive
intermodulation detection system can analyze the received signal to
determine whether an intermodulation product due to a non-linearity
is present. Based on the type of intermodulation product, period,
order, frequency, and etc, the type and location of the
non-linearity can be identified and canceled via a passive
intermodulation canceling mechanism.
Inventors: |
Tsui; Ernest (Pleasanton,
CA), Maxwell; Paul (Piedmont, CA), Kapnadak; Vibhav
(Milpitas, CA) |
Applicant: |
Name |
City |
State |
Country |
Type |
AT&T Intellectual Property I, L.P. |
Atlanta |
GA |
US |
|
|
Assignee: |
AT&T INTELLECTUAL PROPERTY I,
L.P. (Atlanta, GA)
|
Family
ID: |
59828602 |
Appl.
No.: |
15/179,427 |
Filed: |
June 10, 2016 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04B
17/0085 (20130101); H04B 17/3912 (20150115); H04B
17/391 (20150115); H04B 1/109 (20130101) |
Current International
Class: |
H04B
1/10 (20060101); H04B 17/391 (20150101) |
Field of
Search: |
;375/227 |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
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2014166229 |
|
Oct 2014 |
|
WO |
|
2016059424 |
|
Apr 2016 |
|
WO |
|
Other References
Wilkerson et al., "Passive Intermodulation Distortion in Antennas,"
IEEE Transactions on Antennas and Propagation, Feb. 2015, pp.
474-482, vol. 63, No. 2, IEEE. cited by applicant .
Yang et al., "PIM Interference Testing Methods of Satellite
Communication Components and Setting up of the Testing System,"
General Assembly and Scientific Symposium (URSI GASS), 2014, IEEE,
4 pages. cited by applicant .
Tarlazzi, "PIM Requirements Must Increase to Support Evolving DAS
Systems", Commscope, Aug. 2014, 16 pgs. cited by applicant .
Cannon, "Troubleshooting Passive Intermodulation Problems in the
Field", Anritsu America,
http://www.anritsu.com/EnUS/ProductsSolutions/Solution/Troubleshootingpas-
siveintermodulation.aspx, last accessed Mar. 19, 2015, 4 pgs. cited
by applicant .
Office Action for U.S. Appl. No. 14/734,766 dated Jan. 26, 2017, 58
pages. cited by applicant.
|
Primary Examiner: Tayong; Helene
Attorney, Agent or Firm: Amin, Turocy & Watson, LLP
Claims
What is claimed is:
1. A method, comprising: receiving, by a wireless network device
comprising a processor, first signal data related to a signal band,
resulting in a first received signal; filtering, by the wireless
network device using a first filter, modeled interference data
related to modeled interference products of signals; generating, by
the wireless network device, error output data related to a signal
error associated with an actual interference, wherein the error
output data further comprises a representation of the first
received signal; processing, by the wireless network device using
the first filter, the error output data, resulting in processed
error output data; and receiving, by the wireless network device,
second signal data related to the actual interference from the
products of the signals; and cancelling, by the wireless network
device, the actual interference in relation to the modeled
interference.
2. The method of claim 1, wherein the signal band comprises
constituent signal bands.
3. The method of claim 2, wherein the first signal data comprises a
first downlink signal associated with a first signal band of the
constituent signal bands and a second downlink signal associated
with a second signal band of the constituent signal bands.
4. The method of claim 3, wherein the actual interference comprises
products of the first signal band and the second signal band.
5. The method of claim 1, further comprising: processing, by the
wireless network device, an uplink signal associated with the
actual interference resulting in the actual interference being
canceled.
6. The method of claim 1, further comprising: filtering, by the
wireless device using a second filter, the modeled interference
data, wherein the second filter is a finite impulse response
filter.
7. The method of claim 1, further comprising: receiving, by the
wireless device, returned test signal data related to a returned
test signal; and in response to receiving the returned test signal
data, generating location data related to a passive intermodulation
source based on a time delay.
8. The method of claim 1, further comprising: receiving, by the
wireless device, returned test signal data related to returned test
signals; and in response to receiving the returned test signal
data, determining a port associated with the interference.
9. A system, comprising: a processor; and a memory that stores
executable instructions that, when executed by the processor,
facilitate performance of operations, comprising: receiving first
signal data associated with a first downlink of a first signal
band; receiving second signal data associated with a second
downlink of a second signal band; filtering, via a first filter,
modeled interference data related to a modeled interference
associated with the first signal data and the second signal data,
resulting in modeled filtered data; generating error output data
related to a signal error associated with the first signal band and
the second signal band, wherein the error output data comprises the
modeled filtered data; in response to the generating the error
output data, processing, by the first filter, the error output data
to be sent as an input to the first filter; and subtracting the
modeled interference from third signal data related to an actual
signal.
10. The system of claim 9, wherein the error output data comprises
second interference data related to a second interference.
11. The system of claim 9, wherein the operations further comprise:
remotely reconfiguring the system, via a software-defined network,
resulting in a reconfigured system.
12. The system of claim 11, wherein the reconfigured system
arranges system resources based on an environment factor or a third
interference, wherein the third interference appears after an
initial configuration.
13. The system of claim 12, wherein the operations further
comprise: adaptively addressing downlink amplifier nonlinearities
to optimize performance.
14. The system of claim 13 wherein the operations further comprise:
based on a ranking associated with a test signal, successively
filtering the modeled interference data, related to the modeled
interference, resulting in an error reduction.
15. A non-transitory machine-readable storage medium, comprising
executable instructions that, when executed by a processor,
facilitate performance of operations, comprising: receiving first
signal data related to a signal band, resulting in a first received
signal; filtering, by a first filter, modeled interference data
related to a modeled interference of the first received signal,
resulting in first filtered data; generating error output data
related to a signal error associated with the modeled interference;
receiving second signal data associated with an actual signal to be
input into the first filter; and in response to the receiving the
second signal data, removing the first filtered data from the
actual signal.
16. The non-transitory machine-readable storage medium of claim 15,
wherein the operations further comprise: canceling two
intermodulation products of the second signal data
simultaneously.
17. The non-transitory machine-readable storage medium of claim 16,
wherein the two intermodulation products are a largest
intermodulation products.
18. The non-transitory machine-readable storage medium of claim 15,
wherein the first signal data is ranked based on a signal plus
noise to interference ratio.
19. The non-transitory machine-readable storage medium of claim 18,
wherein the first signal data is of a third order.
20. The non-transitory machine-readable storage medium of claim 15,
wherein the first filter is an infinite impulse response filter.
Description
TECHNICAL FIELD
This disclosure relates generally to facilitating the cancellation
of passive intermodulation for antennas.
BACKGROUND
Intermodulation is the amplitude modulation of signals containing
two or more different frequencies in a system with non-linearities
that results in signal noise. The intermodulation between each
frequency component can form additional signals at frequencies that
are harmonic frequencies and sum and difference frequencies of the
original frequencies and multiples thereof. The non-linearities can
be cause by junctions in the physical equipment (cables, antennas),
as well as by sources in the surrounding environment. This type
intermodulation, caused by non-active components, is called
external (in the sense the passive intermodulation sources are
external to the cabling/antenna system) passive intermodulation and
can be difficult and costly to diagnose as site visits by skilled
technicians are traditionally used to detect and identify the
non-linearity source locations.
The above-described background relating to intermodulation is
merely intended to provide a contextual overview of some current
issues, and is not intended to be exhaustive. Other contextual
information may become further apparent upon review of the
following detailed description.
BRIEF DESCRIPTION OF THE DRAWINGS
Non-limiting and non-exhaustive embodiments of the subject
disclosure are described with reference to the following figures,
wherein like reference numerals refer to like parts throughout the
various views unless otherwise specified.
FIG. 1 illustrates an example wireless network comprising passive
intermodulation cancellation according to one or more
embodiments.
FIG. 2 illustrates an example wireless network performing passive
intermodulation detection according to one or more embodiments.
FIG. 3 illustrates an example wireless network performing passive
intermodulation detection according to one or more embodiments.
FIG. 4 illustrates an example wireless network performing passive
intermodulation cancellation according to one or more
embodiments.
FIG. 5 illustrates an example wireless network performing passive
intermodulation cancellation according to one or more
embodiments.
FIG. 6 illustrates example graphs of spectrum of error signals at
various time samples according to one or more embodiments.
FIG. 7 illustrates an example convergence graph according to one or
more embodiments.
FIG. 8 illustrates an example schematic system block diagram for a
method for performing passive intermodulation cancellation
according to one or more embodiments.
FIG. 9 illustrates an example schematic system block diagram for a
system to perform passive intermodulation cancellation according to
one or more embodiments.
FIG. 10 illustrates an example schematic system block diagram of an
example non-limiting embodiment of a computing environment in
accordance with various aspects described herein.
FIG. 11 illustrates an example block diagram of an example,
non-limiting embodiment of a mobile network platform in accordance
with various aspects described herein.
DETAILED DESCRIPTION
In the following description, numerous specific details are set
forth to provide a thorough understanding of various embodiments.
One skilled in the relevant art will recognize, however, that the
techniques described herein can be practiced without one or more of
the specific details, or with other methods, components, materials,
etc. In other instances, well-known structures, materials, or
operations are not shown or described in detail to avoid obscuring
certain aspects.
Reference throughout this specification to "one embodiment," or "an
embodiment," means that a particular feature, structure, or
characteristic described in connection with the embodiment is
included in at least one embodiment. Thus, the appearances of the
phrase "in one embodiment," "in one aspect," or "in an embodiment,"
in various places throughout this specification are not necessarily
all referring to the same embodiment. Furthermore, the particular
features, structures, or characteristics may be combined in any
suitable manner in one or more embodiments.
As utilized herein, terms "component," "system," "interface," and
the like are intended to refer to a computer-related entity,
hardware, software (e.g., in execution), and/or firmware. For
example, a component can be a processor, a process running on a
processor, an object, an executable, a program, a storage device,
and/or a computer. By way of illustration, an application running
on a server and the server can be a component. One or more
components can reside within a process, and a component can be
localized on one computer and/or distributed between two or more
computers.
Further, these components can execute from various machine-readable
media having various data structures stored thereon. The components
can communicate via local and/or remote processes such as in
accordance with a signal having one or more data packets (e.g.,
data from one component interacting with another component in a
local system, distributed system, and/or across a network, e.g.,
the Internet, a local area network, a wide area network, etc. with
other systems via the signal).
As another example, a component can be an apparatus with specific
functionality provided by mechanical parts operated by electric or
electronic circuitry; the electric or electronic circuitry can be
operated by a software application or a firmware application
executed by one or more processors; the one or more processors can
be internal or external to the apparatus and can execute at least a
part of the software or firmware application. As yet another
example, a component can be an apparatus that provides specific
functionality through electronic components without mechanical
parts; the electronic components can include one or more processors
therein to execute software and/or firmware that confer(s), at
least in part, the functionality of the electronic components. In
an aspect, a component can emulate an electronic component via a
virtual machine, e.g., within a cloud computing system.
The words "exemplary" and/or "demonstrative" are used herein to
mean serving as an example, instance, or illustration. For the
avoidance of doubt, the subject matter disclosed herein is not
limited by such examples. In addition, any aspect or design
described herein as "exemplary" and/or "demonstrative" is not
necessarily to be construed as preferred or advantageous over other
aspects or designs, nor is it meant to preclude equivalent
exemplary structures and techniques known to those of ordinary
skill in the art. Furthermore, to the extent that the terms
"includes," "has," "contains," and other similar words are used in
either the detailed description or the claims, such terms are
intended to be inclusive--in a manner similar to the term
"comprising" as an open transition word--without precluding any
additional or other elements.
As used herein, the term "infer" or "inference" refers generally to
the process of reasoning about, or inferring states of, the system,
environment, user, and/or intent from a set of observations as
captured via events and/or data. Captured data and events can
include user data, device data, environment data, data from
sensors, sensor data, application data, implicit data, explicit
data, etc. Inference can be employed to identify a specific context
or action, or can generate a probability distribution over states
of interest based on a consideration of data and events, for
example.
Inference can also refer to techniques employed for composing
higher-level events from a set of events and/or data. Such
inference results in the construction of new events or actions from
a set of observed events and/or stored event data, whether the
events are correlated in close temporal proximity, and whether the
events and data come from one or several event and data sources.
Various classification schemes and/or systems (e.g., support vector
machines, neural networks, expert systems, Bayesian belief
networks, fuzzy logic, and data fusion engines) can be employed in
connection with performing automatic and/or inferred action in
connection with the disclosed subject matter.
In addition, the disclosed subject matter can be implemented as a
method, apparatus, or article of manufacture using standard
programming and/or engineering techniques to produce software,
firmware, hardware, or any combination thereof to control a
computer to implement the disclosed subject matter. The term
"article of manufacture" as used herein is intended to encompass a
computer program accessible from any computer-readable device,
computer-readable carrier, or computer-readable media. For example,
computer-readable media can include, but are not limited to, a
magnetic storage device, e.g., hard disk; floppy disk; magnetic
strip(s); an optical disk (e.g., compact disk (CD), a digital video
disc (DVD), a Blu-ray Disc.TM. (BD)); a smart card; a flash memory
device (e.g., card, stick, key drive); and/or a virtual device that
emulates a storage device and/or any of the above computer-readable
media.
As an overview, various embodiments are described herein to
facilitate passive intermodulation cancellation between network
devices.
For simplicity of explanation, the methods (or algorithms) are
depicted and described as a series of acts. It is to be understood
and appreciated that the various embodiments are not limited by the
acts illustrated and/or by the order of acts. For example, acts can
occur in various orders and/or concurrently, and with other acts
not presented or described herein. Furthermore, not all illustrated
acts may be required to implement the methods. In addition, the
methods could alternatively be represented as a series of
interrelated states via a state diagram or events. Additionally,
the methods described hereafter are capable of being stored on an
article of manufacture (e.g., a machine-readable storage medium) to
facilitate transporting and transferring such methodologies to
computers. The term article of manufacture, as used herein, is
intended to encompass a computer program accessible from any
computer-readable device, carrier, or media, including a
non-transitory machine-readable storage medium.
It is noted that although various aspects and embodiments are
discussed herein with respect to Universal Mobile
Telecommunications System (UMTS) and/or Long Term Evolution (LTE),
the disclosed aspects are not limited to a UMTS implementation
and/or an LTE implementation. For example, aspects or features of
the disclosed embodiments can be exploited in substantially any
wireless communication technology. Such wireless communication
technologies can include UMTS, Code Division Multiple Access
(CDMA), Wi-Fi, Worldwide Interoperability for Microwave Access
(WiMAX), General Packet Radio Service (GPRS), Enhanced GPRS, Third
Generation Partnership Project (3GPP), LTE, Third Generation
Partnership Project 2 (3GPP2) Ultra Mobile Broadband (UMB), High
Speed Packet Access (HSPA), Evolved High Speed Packet Access
(HSPA+), High-Speed Downlink Packet Access (HSDPA), High-Speed
Uplink Packet Access (HSUPA), Zigbee, or another IEEE 802.XX
technology. Additionally, substantially all aspects disclosed
herein can be exploited in legacy telecommunication
technologies.
Described herein are systems, methods, articles of manufacture, and
other embodiments or implementations that can facilitate passive
intermodulation cancellation. A passive intermodulation detection
system can remotely identify passive intermodulation at a base
station site and diagnose the type of intermodulation and location
of the non-linearity that is the source of the passive
intermodulation. The passive intermodulation detection system can
generate a test signal in a first band that is transmitted by an
antenna. Another antenna or the same antenna can receive a signal
in another band, and the passive intermodulation detection system
can analyze the received signal to determine whether an
intermodulation product due to an external or internal
non-linearity is present. Based on the type of intermodulation
product, period, order, frequency, etc., the type (internal or
external or both), magnitude, and location of the non-linearity can
be identified.
The passive intermodulation detection system can also distinguish
passive intermodulation noise from adjacent channel interference.
The remote passive intermodulation test can generate a test signal
that simulates a cellular signal at full or partial load and that
does not impact existing customer site connections and usage so
that the test can be performed while the base station device/cell
tower is in service. Furthermore, using test signals that simulate
spectrum-rich cellular transmissions can generate intermodulation
products that would be generated during real-time use unlike single
tone test signals of traditional passive intermodulation
testers.
Once the characteristics of the passive intermodulation are
discovered and analyzed via test signals or real-time, this
information can then be used to effectively cancel the effect of
the nonlinearities by use of the known characteristics of the test
signals and the estimated characteristics of the nonlinearity
sources.
To cancel a reflected signal, the non-linearity, the multipath to
the passive intermodulation (PIM) source, the reflected signal
multipath, and the reception filtering can be modeled with an
adaptive learning filter. A downlink from two different bands can
transmit two polarizations each (F1 at +/-45.degree.) and (F2 at
+/45.degree.), which can generate four products. Each product can
be tested separately to determine which product dominates in order
to simplify the cancellation and the conversion from a non-linear
system into a linear system. When there is a single band, the test
can determine an internal PIM, but the test can also determine an
external PIM for the single band by isolating the band to determine
if there is any intermodulation on the band. Respectively, a dual
antenna system with adequate isolation can determine
intermodulation for two bands.
Generally, a third order product from a PIM non-linearity is the
strongest. Therefore, canceling the third order product can be the
priority. However, it should be noted that any other order could
potentially be stronger than the third order product. Therefore,
any other order can be selected for cancellation. The products can
comprise transmission signals and complex amplitude and phase
adjustments, wherein the transmissions ports can generate multiple
frequency bands. For example, a PIM source can be modeled to the
third order and give the following in-band products for a band 17
uplink:
(Ka.times.29H).sup.3+(Kd.times.29V).sup.3+[(Kc.times.29H).times.(Kd.times-
.29V)].sup.3+[(Ka.times.17H)(Kc.times.29H)].sup.2+(Ka.times.7V).times.(Kd.-
times.29V).sup.2+(Ka.times.17H).times.(Kd.times.29V).sup.2+(Kb.times.17V).-
times.(Kc.times.29H).sup.2 Eqn (1) The band 17 self-products can be
ignored since they are far away in frequency (but for completeness,
they can appear exactly like the band 29 above except 29 is
replaced by 17).
The PIM can be modeled to determine the largest intermodulation
that impacts the band 17 uplink by placing the third order
intermodulation products into the desired signal. The average
magnitude of the third order intermodulation product components can
be calculated and ranked based on the signal plus interference
noise ratio/physical resource block bandwidth. Thereafter, the two
highest values can be input into the canceller system. However, it
should be noted that one PIM or several PIMs can be cancelled
simultaneously.
The IM system model can comprise a finite impulse response filter
and/or an infinite response filter. The filter can model IM signal
products of a downlink band that was reflected back to account for
the interference coming back from a signal that might have been
delayed by intercepting an object in its path. The interference
could also have a frequency and/or time offset. After the PIM
signal is processed through the filter, White Gaussian Noise (AWGN)
and a desired signal (another uplink signal) can be added to the
filtered signal.
A learning filter can also receive the modeled PIM signal and
produce another output that will be added to the AWGN, a desired
signal (another uplink signal), and the filtered signal, resulting
in an error output. The error output coming out of finite impulse
response filter can be placed through the learning filter to
ultimately converge with the finite impulse response output. The
learning filter can also account for any time delay due to
propagation variance associated with time. The cancelling system
model can also comprise a pre-filter to model the known properties
of any uplink filter to avoid additional adaptive filter taps
associated with the learning filter that can increase complexity
and slow convergence. The pre-filter can also model the reception
antenna and filter.
For these considerations as well as other considerations, in one or
more embodiments, a system comprises a processor and a memory that
stores executable instructions that when executed by the processor,
facilitate performance of operations, comprising cycling a test
signal at a defined rate, wherein the test signal is in a first
band and is transmitted by a transmitter. The operations also
comprise receiving a transmission in a second band by a receiver.
The operations also comprise determining that an intermodulation
product from the test signal is present in the transmission based
on matching a cyclical noise measurement of the transmission to the
defined rate. The operations can also comprise cancelling the PIM
associated with the test signal.
In one embodiment, described herein is a method comprising
receiving first signal data related to a signal band, resulting in
a first received signal, and filtering interference data related to
an interference of the first received signal. Second signal data
related to a second signal can be received, and error output data
related to a signal error associated with the interference can be
generated, wherein the error output data further comprises a
representation of the second signal. Thereafter, the error output
data can be processed, resulting in processed error output data;
and the processed error output data can be input to the first
filter.
According to another embodiment, a system can facilitate, receiving
first signal data associated with a first downlink of a first
signal band, and receiving second signal data associated with a
second downlink of a second signal band. The system can filter, via
a first filter, first interference data related to a first
interference associated with the first signal data and the second
signal data, resulting in first filtered data. The system can also
receive third signal data related to a defined signal, and filter,
via a second filter, the first interference data related to the
first interference associated with the first signal data and the
second signal data, resulting in second filtered data. Thereafter,
the system can generate error output data related to a signal error
associated with the first signal band and the second signal band,
wherein the error output data comprises the first filtered data,
the second filtered data, and the third signal data; and in
response to the generating the error output data, the system can
process, by the second filter, the error output data to be sent as
an input to the first filter.
According to yet another embodiment, described herein is a
machine-readable storage medium that can perform the operations
comprising receiving first signal data related to a signal band,
resulting in a first received signal, and filtering, by a first
filter, interference data related to an interference of the first
received signal, resulting in first filtered data. The
machine-readable storage medium can then receive second signal data
related to a desired signal, and generate error output data related
to a signal error associated with the interference. Consequently,
the machine-readable storage medium can filter, by a second filter,
model data related to a representation of the first filtered data
and the desired signal, resulting in second filtered data, and
input the error output data and the second filtered data into a
third filter.
These and other embodiments or implementations are described in
more detail below with reference to the drawings.
Referring now to FIG. 1, illustrated is an example wireless network
comprising passive intermodulation cancellation according to one or
more embodiments. A passive intermodulation source 100 can reflect
transmission signals 106, 108 from antennas 102, 104. For example,
reflected transmission signal 110 can be reflected to the antenna
102 in response to the passive intermodulation cancellation source
receiving the transmission signals 106, 108. Additionally, it
should be noted that various bands can be associated with the
transmission signals 106, 108. For example, the reception antenna
102 can be associated with band 17 thereby associating band 17 with
the transmission signal 106, and the reception antenna 104 can be
associated with band 29 thereby associating band 29 with the
transmission signal 108. To cancel the reflected transmission
signal 110, the non-linearity of the passive intermodulation source
100, the multipath to the passive intermodulation source 100, the
reflected transmission signal 110 multipath, and the reception
filtering can be modeled with an adaptive learning filter.
Additionally, in an alternate embodiment, pre-passive
intermodulation can be omitted during the modeling.
Referring now to FIG. 2, illustrated is an example wireless network
performing passive intermodulation detection according to one or
more embodiments. A base station site (e.g., a cell tower or other
location where a base station device can be located) can include
one or more remote radio heads that can send transmissions to one
or more mobile devices that are located within range of the base
station site. Non-linearities in passive elements (e.g., antennas,
cabling, junctions between materials, etc) can cause passive
intermodulation when two or more high power tones mix at the
nonlinearities (e.g., junctions of dissimilar metals, rust, and
even loose connectors).
Passive intermodulation detection module 200 can be configured to
detect passive intermodulation caused by nonlinearities at the cell
site. A test signal generator 202 can generate a test signal that
can be transmitted by an antenna. The test signal can be a signal
that includes transmissions at a plurality of frequencies
simulating a transmission sent by the remote radio during normal
operations. The test signal generator 202 can cycle the test signal
at a defined rate and the test signal can be in a first band.
Receiver component 204 can receive, via another antenna, a
transmission in a second band. The test signal can be in a downlink
band, while signals received by the receiver component 204 can be
from a mobile device in an uplink band.
An intermodulation detection component 206 can detect whether the
signal, as received by the receiver component 204 includes any
intermodulation products from passive intermodulation. In an
embodiment, the intermodulation detection component 206 can
determine that an intermodulation product from the test signal is
present in the transmission based on matching a cyclical noise
measurement of the transmission to the defined rate of the test
signal. The intermodulation detection component 206 can also
distinguish the intermodulation product from adjacent channel
interference associated with a signal on an adjacent channel based
on the slope of the noise amplitude as a function of frequency. The
further the frequency is from the adjacent band, the noise
amplitude decreases. By contrast, the intermodulation product from
the passive intermodulation has harmonics that show up as increases
at regular frequency intervals.
The analysis component 208 can determine a type of a source of
non-linearity based on an amplitude and a period of the
intermodulation product. This can also determine characteristics of
the nonlinearity for use in possible cancellation. The analysis
component 208 can also determine a location of the source of
non-linearity based on a time delay between the intermodulation
product and the test signal. The analysis component 208 can also
generate a model of the intermodulation products created by the
non-linearity that is predictive of intermodulation products in
different contexts (band, frequency, amplitude, etc). This model
can then be used by the analysis component 208 to modify or
otherwise process transmissions to mitigate the intermodulation
product on transmissions received by receiver component 204.
The intermodulation detection component 206 and the analysis
component 208 can send their outputs to a cancellation component
210 to cancel reflected signals. The cancellation component 210 can
comprise a finite impulse response filter or an infinite impulse
response filter to filter out products of the downlink band that
were reflected back. The filtering tries to account for
interference associated with a signal that might have been delay by
hitting an object (i.e. a bolt). However, the interference could
also have a frequency and/or a time offset. White Gaussian Noise
can also be added to the cancellation component 210 to assist with
the modeling.
The cancellation component 210 can also comprise a learning filter,
which can be a finite impulse response filter and/or an infinite
impulse response filter. The error output coming from the other
finite impulse response filter can be placed through this learning
filter to ultimately converge with the previous finite impulse
response filter. The learning filter can also account for a time
delay because the propagation can vary with time and a frequency
offset.
Referring now to FIG. 3, illustrated is an example wireless network
performing passive intermodulation detection according to one or
more embodiments. A passive intermodulation detection module 300
can comprise test signal generator 302, a receiver component 304,
an analysis component 306, and a cancellation component 312. The
passive intermodulation detection component can generate a test
signal that can be transmitted by an antenna and the receiver
component 304 can receive, via another antenna, a transmission in a
band from the test signal. The test signal can be in a downlink
band, while the signal received by the receiver component 304 can
be in an uplink band.
The analysis component 306 can comprise a location component 308
that can determine a rough location of the nonlinearity causing the
passive intermodulation based on matching the passive
intermodulation to a sector in which the test signal was
transmitted. A distance component 310 can also determine a location
of the source of nonlinearity based on a time delay between the
intermodulation product received signal and the transmitted test
signal. This time delay can be extracted by any number of
approaches, one of which is to examine the phase vs. frequency
characteristic of the Fourier Transform of the received
intermodulation interference signal cross correlated with the
transmitted signal during a maintenance window (when there are few
if any signals being transmitted) and determine the relative time
of the interference vs. the test signal. In some embodiments, a
radial distance from the receiving antenna can be determined. This
radial distance can be further refined by intermodulation source
reception from other antennas such that intersection of radial
distances from separated antennas may be used to further locate
possible sources of interference.
In an embodiment, based on the estimated location of the source of
the non-linearity and the measured intermodulation products, the
analysis component can generate an intermodulation product model
that can be used to mitigate or cancel the effects of
intermodulation product in received signals via cancellation
component 312. The cancellation component 312 can receive output
from the analysis component 306. The cancellation component 312 can
comprise a finite impulse response filter or an infinite impulse
response filter to filter out products of the downlink band that
were reflected back. The filtering tries to account for
interference associated with a signal that might have been delay by
hitting an object (i.e. a bolt). However, the interference could
also have a frequency or time offset. White Gaussian Noise can also
be added to the cancellation component 312 to assist with the
modeling.
The cancellation component 312 can also comprise a learning filter,
which can be a finite impulse response filter or an infinite
impulse response filter. The error output coming from the other
finite impulse response filter can be placed through this learning
filter to ultimately converge with the previous finite impulse
response filter. The learning filter can also account for a time
delay because the propagation can vary with time.
Referring now to FIG. 4, illustrated is an example wireless network
performing passive intermodulation cancellation according to one or
more embodiments. Transmission signals (ie.: B17DL (740 MHz); B29DL
(722 MHz)) comprising downlink bandwidths at various frequencies
can be input into the passive intermodulation cancellation system
400 as PIM Generation at block 402. The transmission signals can be
passed to a learning filter block 404 and a finite impulse response
filter block 406 either simultaneously or one after the other. It
should be noted that the intermodulation cancellation system 400
can also comprise an infinite response filter. The finite impulse
response filter block 406 can represent the multipath products
(i.e.: Y*H) of the downlink bands that were reflected back to
account for interference coming back from the external PIM sources.
The block 406 can output the products of the transmission signals
that are added to a desired signal (i.e.: B17UL (710 MHz)) and is
combined with Additive White Gaussian Noise (AWGN) to the
transmission signal products at block 408. The result of this
addition, as represented by FIG. 4, can be D=Y*H+V+N. Then the
result (D) can be passed on to block 410 where the intermodulation
interference is canceled by the output of the learning filter block
404 output (i.e.: Y*H.sup.e), resulting in an error output (i.e.:
Y*(H-H.sup.e)+V+N) which can be used to drive the filter update
equations. The desired signal, B17UL including the AWGN can be
passed along to block 412 wherein the error output can be sent back
through the learning filter block 404.
The learning filter block 404 can also be a finite impulse response
filter or an infinite impulse response filter. The error output
from block 410 can ultimately converge with the finite impulse
response filter block 406 output. The learning filter block 404 can
also account for a time delay because signal propagation can vary
with time. Therefore the learning filter block 404 is adaptive and
can be updated during each sampling period or less frequently.
Referring now to FIG. 5, illustrated is an example wireless network
performing passive intermodulation cancellation according to one or
more embodiments. Transmission signals (e.g.: B17TX; B29TX)
comprising downlink bandwidths at various frequencies can be input
into the passive intermodulation cancellation system 500. Prior to
PIM Generation at block 506, the transmission signals can enter a
pre-filter block 502 and be received by a transmission antenna 504.
The pre-filter block 502 can model the uplink filters to reduce the
dimensionality (or length) of the learning filter 514. The
pre-filter block 502 can model a RX antenna and filter block 512.
Thereafter, the transmission signals can be passed to the learning
filter block 514 and a finite impulse response multipath filter
block 508 either simultaneously or one after the other. It should
be noted that the intermodulation cancellation system 500 can also
comprise an infinite response filter. The finite impulse response
filter block 508 can filter out products (i.e.: H) of the downlink
bands that were reflected back to account for interference coming
back from the signals. The finite impulse response filter block 508
can output the products of the transmission signals to be added to
a desired signal (i.e.: B17RX) and Added White Gaussian Noise
(AWGN) to the transmission signal products at block 510. The result
of this addition, as represented by FIG. 5, can be received at a
reception antenna and filter block 512. Then the result of the
reception antenna and filter block 512 can be passed on to block
516 where it can be combined with the learning filter block 514
output (i.e.: H.sup.e), resulting in an error output (i.e.:
Error+B17 RX signals per Pol.). The desired signal, B17RX, can be
passed along to block 518 wherein the error output can sent back
through the learning filter block 514.
Referring now to FIG. 6, illustrated are example graphs of spectrum
of error signals at various time samples according to one or more
embodiments. Graph 600 represents a spectrum of error signal prior
to being sampled. Therefore, the PIM can be represented at
approximately 704 MHz, with an amplitude that is more than the
uplink signal for band 17 at 710 MHz and less than the downlink B17
signal at 740 MHz. After convergence of the adaptive filter, as
indicated by graph 602, the PIM in relation to the band 17 uplink
at 710 MHz is virtually not observable.
Referring now to FIG. 7, illustrated is an example convergence
graph according to one or more embodiments. The FIG. 7 graph
illustrates a sample convergence of a mean squared estimate between
the learning filter output products and the simulated finite
impulse response multipath filter PIM output products. As the graph
indicates, the mean squared estimate reduces as the time samples
increase exponentially.
Referring now to FIG. 8, illustrated is an example schematic system
block diagram for a method for performing passive intermodulation
cancellation according to one or more embodiments. At element 800,
first signal data related to a signal band can be received,
resulting in a first received signal. At element 802, modeled
interference data related to modeled interference products of
signals can be filtered using a first filter, and at element 804,
error output data related to a signal error associated with an
actual interference can be generated, wherein the error output data
further comprises a representation of the first received signal.
Thereafter, the error output data can be processed using the first
filter at element 806, resulting in processed error output data. At
element 808, second signal data related to the actual interference
from the products of the signals can be received, and at element
810, the actual interference can be cancelled in relation to the
modeled interference.
Referring now to FIG. 9, illustrated is an example schematic system
block diagram for a system to perform passive intermodulation
cancellation according to one or more embodiments. At element 900,
first signal data associated with a first downlink of a first
signal band can be received, and at element 902, second signal data
associated with a second downlink of a second signal band can be
received. Consequently, modeled interference data related to a
modeled interference associated with the first signal data and the
second signal data can be filtered, resulting in modeled filtered
data at element 904. At element 906, error output data related to a
signal error associated with the first signal band and the second
signal band can be generated, wherein the error output data
comprises the modeled filtered data. In response to the generating
the error output data at element 906, the error output data can be
processed by the first filter at element 908 and sent as an input
to the first filter. Thereafter, the modeled interference can be
subtracted from third signal data related to an actual signal at
element 910.
Referring now to FIG. 10, illustrated is a block diagram of a
computing environment in accordance with various aspects described
herein. For example, in some embodiments, the computer can be or be
included within the radio repeater system disclosed in any of the
previous systems 200, 300, 400, and/or 500.
In order to provide additional context for various embodiments
described herein, FIG. 10 and the following discussion are intended
to provide a brief, general description of a suitable computing
environment 1000 in which the various embodiments of the embodiment
described herein can be implemented. While the embodiments have
been described above in the general context of computer-executable
instructions that can run on one or more computers, those skilled
in the art will recognize that the embodiments can be also
implemented in combination with other program modules and/or as a
combination of hardware and software.
Generally, program modules include routines, programs, components,
data structures, etc., that perform particular tasks or implement
particular abstract data types. Moreover, those skilled in the art
will appreciate that the inventive methods can be practiced with
other computer system configurations, including single-processor or
multiprocessor computer systems, minicomputers, mainframe
computers, as well as personal computers, hand-held computing
devices, microprocessor-based or programmable consumer electronics,
and the like, each of which can be operatively coupled to one or
more associated devices.
The terms "first," "second," "third," and so forth, as used in the
claims, unless otherwise clear by context, is for clarity only and
doesn't otherwise indicate or imply any order in time. For
instance, "a first determination," "a second determination," and "a
third determination," does not indicate or imply that the first
determination is to be made before the second determination, or
vice versa, etc.
The illustrated embodiments of the embodiments herein can be also
practiced in distributed computing environments where certain tasks
are performed by remote processing devices that are linked through
a communications network. In a distributed computing environment,
program modules can be located in both local and remote memory
storage devices.
Computing devices typically include a variety of media, which can
include computer-readable storage media and/or communications
media, which two terms are used herein differently from one another
as follows. Computer-readable storage media can be any available
storage media that can be accessed by the computer and includes
both volatile and nonvolatile media, removable and non-removable
media. By way of example, and not limitation, computer-readable
storage media can be implemented in connection with any method or
technology for storage of information such as computer-readable
instructions, program modules, structured data or unstructured
data.
Computer-readable storage media can include, but are not limited
to, random access memory (RAM), read only memory (ROM),
electrically erasable programmable read only memory (EEPROM), flash
memory or other memory technology, compact disk read only memory
(CD-ROM), digital versatile disk (DVD) or other optical disk
storage, magnetic cassettes, magnetic tape, magnetic disk storage
or other magnetic storage devices or other tangible and/or
non-transitory media which can be used to store desired
information. In this regard, the terms "tangible" or
"non-transitory" herein as applied to storage, memory or
computer-readable media, are to be understood to exclude only
propagating transitory signals per se as modifiers and do not
relinquish rights to all standard storage, memory or
computer-readable media that are not only propagating transitory
signals per se.
Computer-readable storage media can be accessed by one or more
local or remote computing devices, e.g., via access requests,
queries or other data retrieval protocols, for a variety of
operations with respect to the information stored by the
medium.
Communications media typically embody computer-readable
instructions, data structures, program modules or other structured
or unstructured data in a data signal such as a modulated data
signal, e.g., a carrier wave or other transport mechanism, and
includes any information delivery or transport media. The term
"modulated data signal" or signals refers to a signal that has one
or more of its characteristics set or changed in such a manner as
to encode information in one or more signals. By way of example,
and not limitation, communication media include wired media, such
as a wired network or direct-wired connection, and wireless media
such as acoustic, RF, infrared and other wireless media.
With reference again to FIG. 10, the example environment 1000 for
implementing various embodiments of the aspects described herein
includes a computer 1002, the computer 1002 including a processing
unit 1004, a system memory 1006 and a system bus 1008. The system
bus 1008 couples system components including, but not limited to,
the system memory 1006 to the processing unit 1004. The processing
unit 1004 can be any of various commercially available processors.
Dual microprocessors and other multi-processor architectures can
also be employed as the processing unit 1004.
The system bus 1008 can be any of several types of bus structure
that can further interconnect to a memory bus (with or without a
memory controller), a peripheral bus, and a local bus using any of
a variety of commercially available bus architectures. The system
memory 1006 includes ROM 1010 and RAM 1012. A basic input/output
system (BIOS) can be stored in a non-volatile memory such as ROM,
erasable programmable read only memory (EPROM), EEPROM, which BIOS
contains the basic routines that help to transfer information
between elements within the computer 1002, such as during startup.
The RAM 1012 can also include a high-speed RAM such as static RAM
for caching data.
The computer 1002 further includes an internal hard disk drive
(HDD) 1014 (e.g., EIDE, SATA), which internal hard disk drive 1014
can also be configured for external use in a suitable chassis (not
shown), a magnetic floppy disk drive (FDD) 1016, (e.g., to read
from or write to a removable diskette 1018) and an optical disk
drive 1020, (e.g., reading a CD-ROM disk 1022 or, to read from or
write to other high capacity optical media such as the DVD). The
hard disk drive 1014, magnetic disk drive 1016 and optical disk
drive 1020 can be connected to the system bus 1008 by a hard disk
drive interface 1024, a magnetic disk drive interface 1026 and an
optical drive interface 1028, respectively. The interface 1024 for
external drive implementations includes at least one or both of
Universal Serial Bus (USB) and Institute of Electrical and
Electronics Engineers (IEEE) 1394 interface technologies. Other
external drive connection technologies are within contemplation of
the embodiments described herein.
The drives and their associated computer-readable storage media
provide nonvolatile storage of data, data structures,
computer-executable instructions, and so forth. For the computer
1002, the drives and storage media accommodate the storage of any
data in a suitable digital format. Although the description of
computer-readable storage media above refers to a hard disk drive
(HDD), a removable magnetic diskette, and a removable optical media
such as a CD or DVD, it should be appreciated by those skilled in
the art that other types of storage media which are readable by a
computer, such as zip drives, magnetic cassettes, flash memory
cards, cartridges, and the like, can also be used in the example
operating environment, and further, that any such storage media can
contain computer-executable instructions for performing the methods
described herein.
A number of program modules can be stored in the drives and RAM
1012, including an operating system 1030, one or more application
programs 1032, other program modules 1034 and program data 1036.
All or portions of the operating system, applications, modules,
and/or data can also be cached in the RAM 1012. The systems and
methods described herein can be implemented utilizing various
commercially available operating systems or combinations of
operating systems.
A user can enter commands and information into the computer 1002
through one or more wired/wireless input devices, e.g., a keyboard
1038 and a pointing device, such as a mouse 1040. Other input
devices (not shown) can include a microphone, an infrared (IR)
remote control, a joystick, a game pad, a stylus pen, touch screen
or the like. These and other input devices are often connected to
the processing unit 1004 through an input device interface 1042
that can be coupled to the system bus 1008, but can be connected by
other interfaces, such as a parallel port, an IEEE 1394 serial
port, a game port, a universal serial bus (USB) port, an IR
interface, etc.
A monitor 1044 or other type of display device can be also
connected to the system bus 1008 via an interface, such as a video
adapter 1046. In addition to the monitor 1044, a computer typically
includes other peripheral output devices (not shown), such as
speakers, printers, etc.
The computer 1002 can operate in a networked environment using
logical connections via wired and/or wireless communications to one
or more remote computers, such as a remote computer(s) 1048. The
remote computer(s) 1048 can be a workstation, a server computer, a
router, a personal computer, portable computer,
microprocessor-based entertainment appliance, a peer device or
other common network node, and typically includes many or all of
the elements described relative to the computer 1002, although, for
purposes of brevity, only a memory/storage device 1050 is
illustrated. The logical connections depicted include
wired/wireless connectivity to a local area network (LAN) 1052
and/or larger networks, e.g., a wide area network (WAN) 1054. Such
LAN and WAN networking environments are commonplace in offices and
companies, and facilitate enterprise-wide computer networks, such
as intranets, all of which can connect to a global communications
network, e.g., the Internet.
When used in a LAN networking environment, the computer 1002 can be
connected to the local network 1052 through a wired and/or wireless
communication network interface or adapter 1056. The adapter 1056
can facilitate wired or wireless communication to the LAN 1052,
which can also include a wireless AP disposed thereon for
communicating with the wireless adapter 1056.
When used in a WAN networking environment, the computer 1002 can
include a modem 1058 or can be connected to a communications server
on the WAN 1054 or has other means for establishing communications
over the WAN 1054, such as by way of the Internet. The modem 1058,
which can be internal or external and a wired or wireless device,
can be connected to the system bus 1008 via the input device
interface 1042. In a networked environment, program modules
depicted relative to the computer 1002 or portions thereof, can be
stored in the remote memory/storage device 1050. It will be
appreciated that the network connections shown are example and
other means of establishing a communications link between the
computers can be used.
The computer 1002 can be operable to communicate with any wireless
devices or entities operatively disposed in wireless communication,
e.g., a printer, scanner, desktop and/or portable computer,
portable data assistant, communications satellite, any piece of
equipment or location associated with a wirelessly detectable tag
(e.g., a kiosk, news stand, restroom), and telephone. This can
include Wireless Fidelity (Wi-Fi) and BLUETOOTH.RTM. wireless
technologies. Thus, the communication can be a predefined structure
as with a conventional network or simply an ad hoc communication
between at least two devices.
Wi-Fi can allow connection to the Internet from a couch at home, a
bed in a hotel room or a conference room at work, without wires.
Wi-Fi is a wireless technology similar to that used in a cell phone
that enables such devices, e.g., computers, to send and receive
data indoors and out anywhere within the range of a base station.
Wi-Fi networks use radio technologies called IEEE 802.11 (a, b, g,
n, ac, etc.) to provide secure, reliable, fast wireless
connectivity. A Wi-Fi network can be used to connect computers to
each other, to the Internet, and to wired networks (which can use
IEEE 802.3 or Ethernet). Wi-Fi networks operate in the unlicensed
2.4 and 5 GHz radio bands, at an 11 Mbps (802.1a) or 54 Mbps
(802.11b) data rate, for example or with products that contain both
bands (dual band), so the networks can provide real-world
performance similar to the basic 10BaseT wired Ethernet networks
used in many offices.
In an embodiment of the subject application, the computer 1002 can
provide the environment and/or setting in which one or more of the
passive intermodulation detection and cancellation systems
disclosed in FIGS. 1-5 can be operated from. For instance, the
virtual machines disclosed herein can be applications 1032 stored
in hard drive 1014 and executed by processing unit 1004.
FIG. 1 illustrates an example embodiment 1100 of a mobile network
platform 1110 that can implement and exploit one or more aspects of
the disclosed subject matter described herein. Generally, wireless
network platform 1110 can include components, e.g., nodes,
gateways, interfaces, servers, or disparate platforms, that
facilitate both packet-switched (PS) (e.g., internet protocol (IP),
frame relay, asynchronous transfer mode (ATM)) and circuit-switched
(CS) traffic (e.g., voice and data), as well as control generation
for networked wireless telecommunication. As a non-limiting
example, wireless network platform 1110 can be included in
telecommunications carrier networks, and can be considered
carrier-side components as discussed elsewhere herein. Mobile
network platform 1110 includes CS gateway node(s) 1112 which can
interface CS traffic received from legacy networks like telephony
network(s) 1140 (e.g., public switched telephone network (PSTN), or
public land mobile network (PLMN)) or a signaling system #7 (SS7)
network 1170. Circuit switched gateway node(s) 1112 can authorize
and authenticate traffic (e.g., voice) arising from such networks.
Additionally, CS gateway node(s) 1112 can access mobility, or
roaming, data generated through SS7 network 1170; for instance,
mobility data stored in a visited location register (VLR), which
can reside in memory 1130. Moreover, CS gateway node(s) 1112
interfaces CS-based traffic and signaling and PS gateway node(s)
1118. As an example, in a 3GPP UMTS network, CS gateway node(s)
1112 can be realized at least in part in gateway GPRS support
node(s) (GGSN). It should be appreciated that functionality and
specific operation of CS gateway node(s) 1112, PS gateway node(s)
1118, and serving node(s) 1116, is provided and dictated by radio
technology(ies) utilized by mobile network platform 1110 for
telecommunication. Mobile network platform 1110 can also include
the MMEs, HSS/PCRFs, SGWs, and PGWs disclosed herein.
In addition to receiving and processing CS-switched traffic and
signaling, PS gateway node(s) 1118 can authorize and authenticate
PS-based data sessions with served mobile devices. Data sessions
can include traffic, or content(s), exchanged with networks
external to the wireless network platform 1110, like wide area
network(s) (WANs) 1150, enterprise network(s) 1170, and service
network(s) 1180, which can be embodied in local area network(s)
(LANs), can also be interfaced with mobile network platform 1110
through PS gateway node(s) 1118. It is to be noted that WANs 1150
and enterprise network(s) 1160 can embody, at least in part, a
service network(s) like IP multimedia subsystem (IMS). Based on
radio technology layer(s) available in technology resource(s) 1117,
packet-switched gateway node(s) 1118 can generate packet data
protocol contexts when a data session is established; other data
structures that facilitate routing of packetized data also can be
generated. To that end, in an aspect, PS gateway node(s) 1118 can
include a tunnel interface (e.g., tunnel termination gateway (TTG)
in 3GPP UMTS network(s) (not shown)) which can facilitate
packetized communication with disparate wireless network(s), such
as Wi-Fi networks.
In embodiment 1100, wireless network platform 1110 also includes
serving node(s) 1116 that, based upon available radio technology
layer(s) within technology resource(s) 1117, convey the various
packetized flows of data streams received through PS gateway
node(s) 1118. It is to be noted that for technology resource(s)
1117 that rely primarily on CS communication, server node(s) can
deliver traffic without reliance on PS gateway node(s) 1118; for
example, server node(s) can embody at least in part a mobile
switching center. As an example, in a 3GPP UMTS network, serving
node(s) 1116 can be embodied in serving GPRS support node(s)
(SGSN).
For radio technologies that exploit packetized communication,
server(s) 1114 in wireless network platform 1110 can execute
numerous applications that can generate multiple disparate
packetized data streams or flows, and manage (e.g., schedule,
queue, format . . . ) such flows. Such application(s) can include
add-on features to standard services (for example, provisioning,
billing, customer support . . . ) provided by wireless network
platform 1110. Data streams (e.g., content(s) that are part of a
voice call or data session) can be conveyed to PS gateway node(s)
1118 for authorization/authentication and initiation of a data
session, and to serving node(s) 1116 for communication thereafter.
In addition to application server, server(s) 1114 can include
utility server(s), a utility server can include a provisioning
server, an operations and maintenance server, a security server
that can implement at least in part a certificate authority and
firewalls as well as other security mechanisms, and the like. In an
aspect, security server(s) secure communication served through
wireless network platform 1110 to ensure network's operation and
data integrity in addition to authorization and authentication
procedures that CS gateway node(s) 1112 and PS gateway node(s) 1118
can enact. Moreover, provisioning server(s) can provision services
from external network(s) like networks operated by a disparate
service provider, for instance, WAN 1150 or Global Positioning
System (GPS) network(s) (not shown). Provisioning server(s) can
also provision coverage through networks associated to wireless
network platform 1110 (e.g., deployed and operated by the same
service provider), such as femto-cell network(s) (not shown) that
enhance wireless service coverage within indoor confined spaces and
offload RAN resources in order to enhance subscriber service
experience within a home or business environment by way of UE
1175.
It is to be noted that server(s) 1114 can include one or more
processors configured to confer at least in part the functionality
of macro network platform 1110. To that end, the one or more
processor can execute code instructions stored in memory 1130, for
example. It is should be appreciated that server(s) 1114 can
include a content manager 1115, which operates in substantially the
same manner as described hereinbefore.
In example embodiment 1100, memory 1130 can store information
related to operation of wireless network platform 1110. Other
operational information can include provisioning information of
mobile devices served through wireless platform network 1110,
subscriber databases; application intelligence, pricing schemes,
e.g., promotional rates, flat-rate programs, couponing campaigns;
technical specification(s) consistent with telecommunication
protocols for operation of disparate radio, or wireless, technology
layers; and so forth. Memory 1130 can also store information from
at least one of telephony network(s) 1140, WAN 1150, enterprise
network(s) 1160, or SS7 network 1170. In an aspect, memory 1130 can
be, for example, accessed as part of a data store component or as a
remotely connected memory store.
In order to provide a context for the various aspects of the
disclosed subject matter, FIGS. 10 and 11, and the following
discussion, are intended to provide a brief, general description of
a suitable environment in which the various aspects of the
disclosed subject matter can be implemented. While the subject
matter has been described above in the general context of
computer-executable instructions of a computer program that runs on
a computer and/or computers, those skilled in the art will
recognize that the disclosed subject matter also can be implemented
in combination with other program modules. Generally, program
modules include routines, programs, components, data structures,
etc. that perform particular tasks and/or implement particular
abstract data types.
Furthermore, for future SDN (software defined networks) with NFV
(network function virtualization), the cancellation can be in the
form of a software module that can be implemented (or not if there
is no PIM degradation) inside the virtualized RAN module. This can
scale the approach to include PIM cancellation for PIM originating
from multiple carrier interactions.
In the subject specification, terms such as "store," "storage,"
"data store," data storage," "database," and substantially any
other information storage component relevant to operation and
functionality of a component, refer to "memory components," or
entities embodied in a "memory" or components comprising the
memory. It will be appreciated that the memory components described
herein can be either volatile memory or nonvolatile memory, or can
include both volatile and nonvolatile memory, by way of
illustration, and not limitation, volatile memory (see below),
non-volatile memory (see below), disk storage (see below), and
memory storage (see below). Further, nonvolatile memory can be
included in read only memory (ROM), programmable ROM (PROM),
electrically programmable ROM (EPROM), electrically erasable ROM
(EEPROM), or flash memory. Volatile memory can include random
access memory (RAM), which acts as external cache memory. By way of
illustration and not limitation, RAM is available in many forms
such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous
DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM
(ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DRRAM).
Additionally, the disclosed memory components of systems or methods
herein are intended to comprise, without being limited to
comprising, these and any other suitable types of memory.
Moreover, it will be noted that the disclosed subject matter can be
practiced with other computer system configurations, including
single-processor or multiprocessor computer systems, mini-computing
devices, mainframe computers, as well as personal computers,
hand-held computing devices (e.g., PDA, phone, watch, tablet
computers, netbook computers, . . . ), microprocessor-based or
programmable consumer or industrial electronics, field programmable
gate array, graphics processor, or software defined radio
reconfigurable processor and the like. The illustrated aspects can
also be practiced in distributed computing environments where tasks
are performed by remote processing devices that are linked through
a communications network; however, some if not all aspects of the
subject disclosure can be practiced on stand-alone computers. In a
distributed computing environment, program modules can be located
in both local and remote memory storage devices.
The embodiments described herein can employ artificial intelligence
(AI) to facilitate automating one or more features described
herein. The embodiments (e.g., in connection with automatically
identifying acquired cell sites that provide a maximum
value/benefit after addition to an existing communication network)
can employ various AI-based schemes for carrying out various
embodiments thereof. Moreover, the classifier can be employed to
determine a ranking or priority of the each cell site of the
acquired network. A classifier is a function that maps an input
attribute vector, x=(x1, x2, x3, x4, . . . , xn), to a confidence
that the input belongs to a class, that is, f(x)=confidence(class).
Such classification can employ a probabilistic and/or
statistical-based analysis (e.g., factoring into the analysis
utilities and costs) to prognose or infer an action that a user
desires to be automatically performed. A support vector machine
(SVM) is an example of a classifier that can be employed. The SVM
operates by finding a hypersurface in the space of possible inputs,
which the hypersurface attempts to split the triggering criteria
from the non-triggering events. Intuitively, this makes the
classification correct for testing data that is near, but not
identical to training data. Other directed and undirected model
classification approaches include, e.g., naive Bayes, Bayesian
networks, decision trees, neural networks, fuzzy logic models, and
probabilistic classification models providing different patterns of
independence can be employed. Classification as used herein also is
inclusive of statistical regression that is utilized to develop
models of priority.
As will be readily appreciated, one or more of the embodiments can
employ classifiers that are explicitly trained (e.g., via a generic
training data) as well as implicitly trained (e.g., via observing
UE behavior, operator preferences, historical information,
receiving extrinsic information). For example, SVMs can be
configured via a learning or training phase within a classifier
constructor and feature selection module. Thus, the classifier(s)
can be used to automatically learn and perform a number of
functions, including but not limited to determining according to a
predetermined criteria which of the acquired cell sites will
benefit a maximum number of subscribers and/or which of the
acquired cell sites will add minimum value to the existing
communication network coverage, etc.
As used in this application, in some embodiments, the terms
"component," "system" and the like are intended to refer to, or
include, a computer-related entity or an entity related to an
operational apparatus with one or more specific functionalities,
wherein the entity can be either hardware, a combination of
hardware and software, software, or software in execution. As an
example, a component may be, but is not limited to being, a process
running on a processor, a processor, an object, an executable, a
thread of execution, computer-executable instructions, a program,
and/or a computer. By way of illustration and not limitation, both
an application running on a server and the server can be a
component. One or more components may reside within a process
and/or thread of execution and a component may be localized on one
computer and/or distributed between two or more computers. In
addition, these components can execute from various computer
readable media having various data structures stored thereon. The
components may communicate via local and/or remote processes such
as in accordance with a signal having one or more data packets
(e.g., data from one component interacting with another component
in a local system, distributed system, and/or across a network such
as the Internet with other systems via the signal). As another
example, a component can be an apparatus with specific
functionality provided by mechanical parts operated by electric or
electronic circuitry, which is operated by a software or firmware
application executed by a processor, wherein the processor can be
internal or external to the apparatus and executes at least a part
of the software or firmware application. As yet another example, a
component can be an apparatus that provides specific functionality
through electronic components without mechanical parts, the
electronic components can include a processor therein to execute
software or firmware that confers at least in part the
functionality of the electronic components. While various
components have been illustrated as separate components, it will be
appreciated that multiple components can be implemented as a single
component, or a single component can be implemented as multiple
components, without departing from example embodiments.
Further, the various embodiments can be implemented as a method,
apparatus or article of manufacture using standard programming
and/or engineering techniques to produce software, firmware,
hardware or any combination thereof to control a computer to
implement the disclosed subject matter. The term "article of
manufacture" as used herein is intended to encompass a computer
program accessible from any computer-readable device or
computer-readable storage/communications media. For example,
computer readable storage media can include, but are not limited
to, magnetic storage devices (e.g., hard disk, floppy disk,
magnetic strips), optical disks (e.g., compact disk (CD), digital
versatile disk (DVD)), smart cards, and flash memory devices (e.g.,
card, stick, key drive). Of course, those skilled in the art will
recognize many modifications can be made to this configuration
without departing from the scope or spirit of the various
embodiments.
In addition, the words "example" and "exemplary" are used herein to
mean serving as an instance or illustration. Any embodiment or
design described herein as "example" or "exemplary" is not
necessarily to be construed as preferred or advantageous over other
embodiments or designs. Rather, use of the word example or
exemplary is intended to present concepts in a concrete fashion. As
used in this application, the term "or" is intended to mean an
inclusive "or" rather than an exclusive "or". That is, unless
specified otherwise or clear from context, "X employs A or B" is
intended to mean any of the natural inclusive permutations. That
is, if X employs A; X employs B; or X employs both A and B, then "X
employs A or B" is satisfied under any of the foregoing instances.
In addition, the articles "a" and "an" as used in this application
and the appended claims should generally be construed to mean "one
or more" unless specified otherwise or clear from context to be
directed to a singular form.
Moreover, terms such as "user equipment," "mobile station,"
"mobile," subscriber station," "access terminal," "terminal,"
"handset," "mobile device" (and/or terms representing similar
terminology) can refer to a wireless device utilized by a
subscriber or user of a wireless communication service to receive
or convey data, control, voice, video, sound, gaming or
substantially any data-stream or signaling-stream. The foregoing
terms are utilized interchangeably herein and with reference to the
related drawings.
Furthermore, the terms "user," "subscriber," "customer," "consumer"
and the like are employed interchangeably throughout, unless
context warrants particular distinctions among the terms. It should
be appreciated that such terms can refer to human entities or
automated components supported through artificial intelligence
(e.g., a capacity to make inference based, at least, on complex
mathematical formalisms), which can provide simulated vision, sound
recognition and so forth.
As employed herein, the term "processor" can refer to substantially
any computing processing unit or device comprising, but not limited
to comprising, single-core processors; single-processors with
software multithread execution capability; multi-core processors;
multi-core processors with software multithread execution
capability; multi-core processors with hardware multithread
technology; parallel platforms; and parallel platforms with
distributed shared memory. Additionally, a processor can refer to
an integrated circuit, an application specific integrated circuit
(ASIC), a digital signal processor (DSP), a field programmable gate
array (FPGA), a programmable logic controller (PLC), a complex
programmable logic device (CPLD), a discrete gate or transistor
logic, discrete hardware components or any combination thereof
designed to perform the functions described herein. Processors can
exploit nano-scale architectures such as, but not limited to,
molecular and quantum-dot based transistors, switches and gates, in
order to optimize space usage or enhance performance of user
equipment. A processor can also be implemented as a combination of
computing processing units.
What has been described above includes mere examples of various
embodiments. It is, of course, not possible to describe every
conceivable combination of components or methodologies for purposes
of describing these examples, but one of ordinary skill in the art
can recognize that many further combinations and permutations of
the present embodiments are possible. Accordingly, the embodiments
disclosed and/or claimed herein are intended to embrace all such
alterations, modifications and variations that fall within the
spirit and scope of the appended claims. Furthermore, to the extent
that the term "includes" is used in either the detailed description
or the claims, such term is intended to be inclusive in a manner
similar to the term "comprising" as "comprising" is interpreted
when employed as a transitional word in a claim.
* * * * *
References